Accurate assessment of water quality is crucial for protecting public health and promoting environmental sustainability. Conventional laboratory-based methods for evaluating microbial contaminants are often time-consuming, resource-intensive, and reactive in nature, limiting their effectiveness for real-time water quality monitoring and management. This study examines the application of data-driven machine learning models to predict E. coli concentrations in Midmar Dam, utilizing readily available physicochemical parameters. A comparative analysis was conducted using five classical standalone ML algorithms: Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost). These models were assessed based on their predictive performance using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the models evaluated, the kNN algorithm demonstrated superior performance, achieving the lowest MSE and RMSE values, thereby highlighting its effectiveness in capturing the complex relationships between physicochemical indicators and microbial contamination levels. The findings demonstrate the potential of ML-based approaches to serve as efficient, scalable, and proactive tools for sustainable water-quality monitoring and management in dams.

Predicting algal proliferation in freshwater systems is crucial for effective water quality management and ecological sustainability. This study proposes a novel data-driven framework that integrates correlation-based feature ranking with a concatenation-enhanced artificial neural network (ANN) architecture to improve algae prediction accuracy. The analysis was conducted through a systematic evaluation of parameter relationships, employing Pearson’s correlation coefficient and standardized coefficients (Beta) to determine feature importance. Based on the magnitude of these coefficients, the input variables were progressively grouped into six feature sets, enabling a comparative assessment of predictive performance. The ANN models were trained and validated using root mean squared error (RMSE), mean absolute error (MAE) and Normalized Nash–Sutcliffe Efficiency (NNSE) as evaluation metrics. The results demonstrate that the fourth feature set, including chlorophyll-a, temperature, dissolved oxygen, total dissolved solids, and ammonia (NH₃), identified through combined Pearson and Beta analysis, achieved the lowest prediction errors and superior generalization performance. These findings highlight the effectiveness of feature selection guided by correlation and standardized coefficients in enhancing ANN performance for algae prediction. The proposed framework offers valuable insights for improving the predictive modeling of algal dynamics, thereby supporting proactive water quality monitoring and the sustainable management of aquatic ecosystems.

Water quality monitoring is essential for environmental protection, public health, and sustainable water resource management. However, many important water quality parameters remain difficult or expensive to measure continuously using conventional sensing techniques. In recent years, Machine Learning (ML)-based soft sensing has emerged as an effective alternative for estimating hard-to-measure variables using readily available environmental and sensor data. This paper presents a comprehensive survey and tutorial on ML-based soft sensing in water quality monitoring. First, the study reviews and classifies existing approaches, including linear regression, support vector machines, tree-based ensemble methods, artificial neural networks, and advanced deep learning architectures. Second, the paper discusses major challenges, emerging trends, and research gaps related to data quality, sensor drift, interpretability, transferability, uncertainty quantification, and real-world deployment. Third, a tutorial-style framework is presented covering data preprocessing, feature engineering, model selection, training, evaluation, explainability, and deployment considerations for practical soft sensing development. The review further highlights emerging directions involving explainable artificial intelligence, edge intelligence, digital twins, remote sensing integration, and intelligent cyber–physical monitoring systems. Overall, this study provides researchers and practitioners with a comprehensive reference for developing reliable, scalable, and interpretable ML-based soft-sensing systems for water-quality monitoring.